{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "202be4dd-75c1-48ab-8ea1-2b1fa4eadf64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b47af89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2mAudited \u001b[1m5 packages\u001b[0m \u001b[2min 11ms\u001b[0m\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!uv pip install numpy pandas scikit-learn seaborn matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a3f0df9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Generate some sample data\n",
    "np.random.seed(0)\n",
    "X = 2 * np.random.rand(100, 1)\n",
    "y = 4 + 3 * X + np.random.randn(100, 1)\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame(data=np.hstack((X, y)), columns=[\"X\", \"y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "465df04a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: 4.222151077447228\n",
      "Coefficient: 2.9684675107010197\n"
     ]
    }
   ],
   "source": [
    "# Train a simple linear regression model\n",
    "model = LinearRegression()\n",
    "model.fit(df[[\"X\"]], df[\"y\"])\n",
    "\n",
    "# Print the coefficients\n",
    "print(f\"Intercept: {model.intercept_}\")\n",
    "print(f\"Coefficient: {model.coef_[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f4cb6bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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M9omMtomO9omO9sl82+jnYSyfe5YH2ViVlpbK8OHD5ZdffjH1tMnQQKxznkWivWDNmzeXevXqER5gGf0j1p7ZDRs2yPr1663eHVtZsWKF1btga7RPZLRNdLRPdLRPZtumbt267giyWkZwww03yLfffitjx46Vzp07J7U9Dao6cW+4coLff/9dWrZsmXSPrwYQgnB4tE98baPH67p162SvvfbyfJmBfuPXN0utma9fv77Vu2M7tE9ktE10tE90tE/m22bZsmUxPc72QVY/wK+88kpzqvW5556TQw45JOltakAId/UJndZL6xIbNGiQVGDQQOx/Hq8Hj3Bon/jaRo9H7ZXVQJuVlWXxHtqDvllydZ3IaJ/IaJvoaJ/oaJ/MtU2sHV22DrJbt26VSy+91PTIajlBhw4dMvK89BLCTjgeAQBwYJC9//77ZdWqVTJx4kRzqj+wTlDv05sHAADgXbXtfIpVL36gA7O0VzbUxx9/LHvssYcl++YEOhNE4FRm/gFsOhfv0KFD46oB1rrNmTNnSo8ePczo+USMGTPG1DeHFnHvtttu0qdPH7nqqqtinopLe+b1S87ZZ58d0+N1gKC2xbHHHpvQvgMAAHuyVZB94IEHKv9fe1t/+OEHS/fH6XQeXv3x1/8uXbpUHnroIenfv7+89tprZlqnWMydO1duvfVW8+UhGa1bt5Y33njDfEnRAU16ynzOnDny73//2wTtK664Iqbt/Pe//41539WgQYNMWCbIAgDgLsxG72JadN2iRQvzs+eee8rxxx8vzz//vPz111+mXCOeHtlU0C8n/v3R3uHdd99d+vXrJ0cccYS5Wlus9PcZ9AQAAAiyaZZfUCyr1+VL3spN5lbvW0lP5Z944onyzjvvVC7TnlrttdQZIfRiE/7Aq7755hu55JJLzP/r8unTp5v/f/3116VXr15mKrSuXbvKRRddJIsWLUo44AbOFffZZ59J3759JTc3V4466ihTRqA9yoGlBf790J5i/Rk9erQJxF26dDGvRS99HFhioWUNF198sVn2+eefm7IEfaz+jv6+DiwEAMDL8m2WWWJBkE2jTdt2ykNT5sk1oz+Rm5+cY271/vot1l4ZpH379mYQ3Y4dO8z8b1p+0KRJE3n11Vfl7bffllNOOcUEQ71ohIZJrW/1h9fTTjtNPvzwQxk1apQMHDhQ3nvvPXO1NS0VuPPOO+PaDw2nGki//PJLOfXUU80y3fY111xjygB03d13321qpW+66aaI29F93rJli0yZMkWeffZZc9njxx9/3KzTUgYtadDXqK9j06ZNMnjwYDnnnHPMdjXgaunEgw8+mFSbAgDgZOu3FNoysziqRtZNiktFxrz+vSzIC74ik94fM22BDOvfXRpmV3/FinRo1KiRudVpzbQ2VXtc9RR/Tk6OWT5kyBBTepCXlycdO3aUxo0bm+U6QExP6Wvovffee+XMM880y7VE4NxzzzXhNpo///zTBGN/qYKGaK111cF8/l7fZ555xvQYX3vtteb+PvvsYx5/3XXXmcmRw13IQrehz62vpW3btiZsa6+rf5+1x1fLLHS/NZwXFxebnmndb/15+umnK+dvBQDAa/ILik02sWNmqQ5BNk3yC8uqHBB+unzr9p2WHRT5+fmVE+1reNWyAO3VXLx4sbmy2c8//2zWl5eXh/19LUH49ddf5amnnpLffvtNVq5caUJvpMf76RXTJk+ebEKjhkkNlq1atQqaJ1XLHE4//fSg3zv00EMr14ULsnrFKw2xgcFWZ7sIR4P5GWecIVdffbWptT3yyCNN76+GZwAAvGjr9p22zSzVobQgTQqKwgcpvx2F0denk55610vJaYjVuXm1Z1XLBjRUaqidMWNG1N/XgVn6O1qecPDBB8stt9xi6kyrU7t2bdl7773Njw4+0yAZOtl/uIFl/oCsv5/otZgDPfLII6YkQksjNm/eLMOGDYt5xgQAANxmR6F9M0t16JFNk+ys//UQhpNTP/r6dFmzZo2ZRksv+xtYX/rBBx9U9mpq72pgqAwNm3r6X0sJtH7Vzz81l/5OMlei0oFc8+fPl8suu6xy2bx588ytlg0ka+HChWag2+233y777ruveZ5Zs2aZMLtx48aE58kFAMCpcurbM7PEgiCbJg3r15LcDi3CdtXr8sYN6qV9HwoKCiqvhqYDqzSg6iAovZDEgAEDzHIdCKW1qu+//75069bNlAroLAFKT/8r/7WTteRgl112kV133dWETe3Z1dP4n3zyiRlo5f+devUSf23aS6oXbBg3bpwZALZixQq55557pGfPngkHWe151u1s2LDBlFO8/PLLJrTrzAg6SE0HfWkPtb42AAC8pnGDepZnlkRRWpAmdWuLXH9eV3MABNL7Q/rmZqTWRKfQ0umr9EdDoc5EoFNoaZDzD+zSGQr0tLpejEIfc99995neVq2D9U+npbMc6BXBbrjhBnMhhREjRph5YPXCCuedd558+umnlaP+E52Cy+/kk0+WRx991Jz61+m9Ro4caWpm/bMQJEKn3dIpvXTmAg3DOnvB119/LWeddZZceOGFZjCYznYQ65XFAABwk4bZdeX6vrmWZpZE1fClarZ7h/AHrU6dOlVZp72Wy5cvNyPlk5lwXwcz6bZ0GwU7y0yRtNaXaNe8fqux8wGRCYHtoyES0dsmVcelG+hZBp15Qgft+c8U4H9on8hom+hon+i80j75BcVxZ5Z0tU20vBaI0oI00wPA68EVAADYX0MHZhbOpQIAAMCRCLIAAABwJIIsAAAAHIkgCwAAAEciyAIAAMCRCLIAAABwJIIsAAAAHIkgCwAAAEciyAIAAMCRCLIudfHFF0uHDh2Cfg466CA59thjZdSoUVJYWChueI233nprWp9D22369OmW7gMAAAiPS9S62Kmnnip33HFH0PWQ//vf/8r9998v5eXl8q9//UucbMyYMVKrVi3P7wMAAF5FkHWxrKwsadGiRdCyvffeW3788Ud59913HR9kmzRpYvUu2GIfAADwKkoLYuXziezYYc2PPncK1atXT2rX/t93mOLiYnnooYfk6KOPltzcXOnbt6/puQ2k9/v06SOdOnWSM844Q958801z2n316tVm/XHHHSejR4+W0047TQ477DD59ttvxefzybPPPivHH3+8dOnSRXr37i2zZs0K2u5zzz0nJ5xwgil70G089dRT5veUlj9oj/KRRx5pnvess86S//znPxFP6y9YsEAuueQS6datm9mH2267TTZv3ly5Xrevz3f99deb16mP+fe//y2lpaUJt2XgPmgJwoknnlh5q6/p7LPPlu+++y6utgYAALEhyMZCg9VRR4k0aBDTT63GjSWnVStzG+vvRP05+uiUhFkNbJ999pm89dZbJlT6aeD78ssv5eGHH5YZM2aYkoSrr77aPFYtWbJEBg0aJEcccYT53WuuucaE1lBTpkyRO++8UyZOnChdu3aVxx57TF555RUZMWKEzJ4924RM7QXWZerTTz+VCRMmyN13320C6s033yzjx4+vDLtPPPGE5OXlyTPPPGN6kHv06CE33nhjZXgO9MMPP5hQud9++8m0adPM7y5cuFCuuOIKKSsrq3ycLj/kkEPMcwwfPtzs89tvvy2p8tdff8mrr75qwqq2Zf369U3Q9Yfz6toaAADEjtKCWNWoYfUexE3D4wcffFB5v6ioSHbbbTcT7jQ8qZUrV5ogN3PmTOnYsaNZNmDAAPn5559N76UODps0aZLpXdTgp/bdd1/ZuHGj3HvvvUHPd8wxx8g//vGPynpc/b1HH33UbEPttdde8scff5jtau/uqlWrpG7durL77rub/dKfli1bmlv1+++/S05Ojuy5557SqFEjGTp0qAmhjfULQojnn3/e9BBraFZt27Y1z62BXXs8dd/UUUcdZQK10u1OnjxZ5s+fb3p7U6GkpMQE88C2vO6662T9+vWmh7m6tgYAALEjyMYaYufM0XQW08O1B1BDo9aopmQgUHZ2QkFaT6VrL6f2BmqPpQZPDZoaYv2lBYsXLza3F110UZVApuHR/xh/QPXTQBlK62/9li1bJjt37pR//vOfUrNmzaBeYT29ru3Tq1cvcxr+5JNPlnbt2pnn0P/3B9krr7zS7Kv2BHfu3NmUGOjvNGzYsMpzL1261KwPtP/++5vHaq+uP8hqwA2k6/W1plLgc/j3VZ8jlrYGAACxI8jGSoNkTk5sj9VT2Rpgs7Iqbi2ivZn+cNmmTRvT26k9gBqu/QO9/Ke8p06dah4fyB9A9fE6y0F1NLj7+bf7+OOPmx7c0KCvPbHZ2dmmVEFrW/V0u/acvvTSS6aGdfDgwaaG9PPPPzfrvvrqK9OTqaUHWrqg4TaQ//lC6fI6depU3tfnDfeYVIr0HLG0NQAAiB2fnh5y+OGHmyCrNapffPGFWaY1pUpPfWvo9f9oT6l//lTt2dQe3UAaPqPR8Kq9vn/++WfQdjWYvvDCCya4aemD7osOzhoyZIipbT3vvPNMPax68sknzUApHSymtbdaJqHlAIHlEn5aVhA4qErpKfvt27dX6YW1SixtDQAAYkeQ9RitM9XeWe2R3bFjhwlXPXv2lJEjR8onn3xi6lZ1pgEdhKU1reryyy+XRYsWmQFKy5cvlw8//NCETFUjQsmDnlK/4IILzOAq7XXV7b7xxhtmEJR/SjAtMdBBY9rTqgO45s2bJ3PnzjU9sUp/R/dLe2O1tlYDrAZj//pAGtC1hOCee+6RX3/9Vb755htTVnHAAQdU6b2Nl5YtaPAP/AkN9rGIpa0BAEDsKC3wGJ16S8OeDnjSWQW0p1Nv9eeuu+6SrVu3mlCl9bQ6IEu1b99exo4dawZP6QCuffbZR/r3728uBhB42j6UjtDfZZddTJhdt26d7LrrrqbnVUOn1s+ec8455vnGjRtnRvvrIC6tkdUAqjTwadAdNmyYbNmyxQwK03WBMy746fReWnKgpQw6cKtBgwZmWi+t0Y22j7HQHmT9CXTooYeagWLxqq6tAQBA7Gr4Ul0gaHPas6h0XtJQOgBJexw1qAXWe8Yr5YO9LKa9j1omoL2bfloWcPvtt5sSg8A5ab3YPqkUrm1SdVy6gc6GodPB6awPWmONYLRPZLRNdLRPdLRP5tsmWl4LRGkBqqUHqPbgfvzxx+bUvp7q197Y008/Pe4QCwAAkCqkEFRLrz6lA5Tuu+8+Wbt2rTRr1syEWC0TAAAAsApBFtXSAV06HZb+AAAA2AWlBQAAAHAkgiwAAAAciSAbhscmcoDNcTwCABAeQTaAf75RnUoCsAv/8ZjsfLgAALgNg70C6LydTZo0MZP3K50PLdKVq6qbC1Qn/PdvE8Fon9jaRi/jqyFWj0c9LmkrAACCEWRDtG7d2tz6w2wiysvLpbS01MyxqmEEwWif+NpGQ6z/uAQAAP9DkA2hPbB6KdWWLVtKSUlJQtsoLCyU3377zVx+tH79+infR6ejfWJvGy0noCcWAIDwCLIRaHhINEBor5qqV6+e5y8pGg7tExltAwBA7DivCwAAAEciyAIAAMCRCLIAAABwJIIsAAAAHIkgCwAAAEdi1gIAAOBI+QXFsnX7TtlRWCI59etI4wb1pGF2Xat3CxlEkAUAAI6zfkuhjJm2QBbkra9cltuhhVzfN1daNGGOcq+gtAAAADiuJzY0xCq9r8t1PbyBIAsAABxFywlCQ6yfLtf18AaCLAAAcBStiU1mPdyDIAsAABxFB3Ylsx7uQZAFAACOorMT6MCucHS5roc3EGQBAICj6BRbOjtBaJjV+0P65jIFl4cw/RYAAHAcnWJrWP/uzCPrcQRZAADgSBpaCa7eRmkBAAAAHIkgCwAAAEeitAAAAMBG9Mpk1P7GhiALAABgE+u3FFa5/K7OxqCzNOgANwSjtAAAAMAmPbGhIVbpfV2u6xGMIAsAAGADWk4QGmL9dLmuRzCCLAAAgA1oTWwy672IIAsAAGADOrArmfVeRJAFAACwAZ2dIPSyu366XNcjGEEWAADABnSKLZ2dIDTM6v0hfXOZgisMpt8CAACwCZ1ia1j/7swjGyOCLAAAgI1oaCW4xobSAgAAADgSQRYAAACORGkBAACAA+UXFHu+lpYgCwAA4DDrtxRWuZytzm6gsx7ogDGvoLQAAADAYT2xoSFW6X1druu9giALAADgIFpOEBpi/XS5rvcKgiwAAICDaE1sMuvdhCALAADgIDqwK5n1bkKQBQAAcBCdnSD0MrZ+ulzXewVBFgAAwEF0ii2dnSA3JMzq/SF9cz01BRfTbwEAADiMTrE1rH935pG1egcAAAAQv4bZdT0XXENRWgAAAABHIsgCAADAkWwVZCdMmCAXX3xx0LIlS5ZI//79pWvXrnLcccfJSy+9ZNn+AQAAeEpJiUjv3iIHHijy3XdiN7YJslOnTpXHH388aNnmzZtlwIABstdee8mbb74p1113nTz88MPm/wEAAJAmxcUivXqJ1K0rMmuWyOLFIu+/L3Zj+WCvtWvXysiRI+Wbb76RNm3aBK2bNm2a1KlTR0aNGiW1a9eWtm3bysqVK+WZZ56Rc845x7J9BgAAcG2A7dNH5N13g5dnZYn8859iN5b3yP70008mrM6aNUu6dOkStG7evHly6KGHmhDrd/jhh8uKFStkw4YNFuwtAACAC61dK1Kjhki9esEhtn59kVWrRAoLK8KszVjeI6t1r/oTzpo1a6R9+/ZBy1q2bGlu//rrL2nevHlG9hEAAMCV/vpLZLfdqi7PyRHJyxPZfXexM8uDbDRFRUVSV2szAtTTbwoisnPnzoS36/P5pKCgQNKlUL+1BNwiGO0TGW0THe0THe0TGW0THe3jvfap8eefUn+//cKuK5ozR8oPPrjiTjV5KV1to1mthvYQOznIZmVlSbHWagTwB9js7OyEt1tSUmJmQ0g3LYFAZLRPZLRNdLRPdLRPZLRNdLSP+9unzpo10vmMM8KuW3HnnbLxrLMq7sSZk9LRNqGdmY4Lsq1bt5Z169YFLfPfb9WqVcLb1Zrcdu3aSbrotxL9B9XBa/W1tgRBaJ/IaJvoaJ/oaJ/IaJvoaB97ts/OUpH8glIpKCqRnKw60iC7ttRLMLnV+OUXqd+1a/jnGT9eyi65RLR4s6KA0/q2WbZsWUyPs3WQPeSQQ+TVV1+VsrIyqVWrlln29ddfyz777CPNmjVLeLvaVZ1Mj26s9B80E8/jVLRPZLRNdLRPdLRPZLRNdLSPfdpn/ZZCGTNtgSzIW1+5LLdDC7m+b660aBJHYPz5Z5GOHcOv07n5L75YKoo27dU2sZQV2GLWgmh0iq3t27fLHXfcYZL59OnTZdKkSTJo0CCrdw0AACAt8guKq4RYpfd1ua6v1k8/VcxCEC7ETp6sRagmxDqdrYOs9rpOnDhRli9fLn369JGxY8fK8OHDzf8DAAC40dbtO6uEWD9drusjWrSoIsAedFDVdU8+WRFg+/cXt7BVacEDDzxQZVnnzp3ltddes2R/AAAAMm1HYUn867//XiQ3N/wvjBsncs014ka2CrIAAFhBT9VqL5cGhJz6daRxg3rSMLv6EdNAOugxGPP6OXNEevQI/8BnnhG58kpX/w0QZAEAnhZpUM1VZ3UyZ2gb5TjjAx3uoSFSj8Fw5QW6XNfLO++IRJhGS55/XmTAgMwPLLOArWtkAQCwalDN09MXySfzVstDU+aZD3ogU/SLk4ZIDZOB9P7NDVZLw5x64UPsSy9V1MDGEWJTMrDMQvTIAgA8K9qgmoW/rJfePfaVaR8tNR/ow/p3p2cWGaM9oXrM+U/3N//4XWl2xd8XKwg1apTIiBFpG1hm5+OeIAsA8KzqBtUUl5Q75gMd7qPHW8O3Z4qcf374B9x3n8htt2V+YJmNUFoAAPCs6gbV1K1T0zEf6HCZp5+umEYrXIh96KGKEoLbkguxcQ8ssyGCLABAvD6oJpwu+7WQn1dudswHupW0jnL1unzJW7nJ3Nq9rtLW7r23IsCGmy7r8ccrAuzNN2fkbyDXP7DMxigtAACI1wfVhA520RB75tH7moFeTvlAt4qTR7zbysiRFbWu4YweLTJ8eEb/BnI7tJAhfXNtX05DkAUAeFrgoJr8HSVSXFomPyzbYEJsUXGZYz7QrVDdiHenDpALnVM1J6uW1KyZppPYt98ucv/9kXtndX2GB5blMI8sAAAOG1Tz94e2hphmjbPk0ANaOeoD3QpOH/EeTw/zwDM6pPaJtDzgkUdSPgtBKv4GnIQgCwCACz7QreD0Ee/x9DBP9InceGFXyU72SYYMERkzJvw67Zm99dZkn8FTCLIAACCh0Fevbi259ZJDzOwOOjBu1he/mnIMpw6Qi9rDvHS95BeUyi6NEty4Dt7SmQjC0Z7Zm25KcMPeRpAFAABJn37XAXJaZxlYW+y0AXLV9SAXFJXGv9Errqi4ZGw4Tz4pcv318W8TlQiyAAAg6dPveiU0dWaPtvLLqs2OHCBXXQ9ydlYcsal/f5GpU8OvGz9e5Oqr49w7hMM8sgAAIGWX9T26626mZ7a5A6feijqnavsW0jA7hiDbt2/FPLDhQuzEiRXzwBJiU4YgCwAAUnb6fWdxmeN6YkPnVA0Ns2bWgl4dpF60HNu7d0WAff31qutefLEiwGqZAVKK0gIAAOCZS5omMqeqziO74tc8ad4kzBRcp5wi8sEH4Tf28ssiF16Y9n32MoIsAACI+/R7uPICJw7wimUKtoKCAikvLw9+UM+eIp99Fn4D06aJnHdemvcSitICAACQktPvThzgFbd//KOihCBciJ0xo6KEgBCbMfTIAgAAz1zSNFEd+/WT7Ly88Cvfflvk9NMzvUsgyAIAgER45gpoBx4o2YsXh1/3/vsiJ5+c6T1CAIIsAABAqHbtRH79Nfy6jz8WOe64TO8RwiDIAgDSPoG+l05Bw+H22EPkjz/Crip6/33JogfWVgiyAICMXspUBwXpYCGtswRso3lzkY0bw64q+ugj+alJE+nYsWPGdwvRMWsBACCjlzLV+7pc1wOWa9CgYhaCcCH266/NLATlRxxhxZ4hBvTIAgAyfilTXa7rKTGAZWrXFikrC79u7lyR7t0zvUe2lW/j8iCCLADAkkuZVrceSAvtfY1kwQKRrl0zuTe2t97m5UGUFgAA0sLtlzKFAwNspBD7ww8VFzIgxDquPIggCwBI66VMw3HLpUytoOFh9bp8yVu5ydzaIUw4NsDq/LAaYDt1yvReuaY8yGqUFgAA0nop03CnJT1xKVMPnuZ1TAmBXqGrfXtP1JCmuzyoaYNaYiWCLAAgbbx4KVOrTvNqO9Ou1QTYZctE2rZN6dO5+ctFjgPKgygtAACklYarPVo2lA57NzW3hC33nua1bQnB8uUVJQQpDrFOqCF1e3kQQRYAAAdgFogEAuzvv1cE2DZt0vLUbvpykR+m9tpfHhQaZu1UHkRpAQAADuCE07y2KSFYvVpk993Tvgtu+XKxvpryCDuXB9EjCwCAAzjhNK/lPbAaYLUHNgMh1i1fLvJjKI+wc3kQQRYAPIgpnJzHCad5LQuwf/yR0QDrpi8XTi+PoLQAADzGzaOs3c7up3kzXkKgAXa33cQqbphibofDyyMIsgDgIUzhZI1UzjOqv+eJf6NoAXbNGpFWrcQOnP7lIsfh5REEWQDwkFhOIzrlA9gp6AFPYYBdu1akZUuxGyd/uWj8d3lEuPcFJ5RHUCMLAB7i9NOITuP2eUYzVgO7YUNFDawNQ6zTNXR47TU9sgDgIU4/jeg09IAn2QO7ebNIkyaZ3BtPauHg8giCLAB4iNNPIzoNPeAJBtitW0UaNcrk3nheQ4eWR1BaAAAe4vTTiE5DD3icJQT5+RUlBITYmOV7fCo9emQBwGOcfBrRaegBj7EHdscOkezsTO6NK6xnICE9sgDgRXa+Uo+b0ANeTQ9sYWFFDywhNm4MJKxAjywAAGnk2R7waD2wRUUi9TzUG50GDCSsQJAFACDNnDqQJuUBtrhYpI4H64LTgIGEFQiyAAAgvQG2pESkNpEjlRhIWIEaWQAAkJ4a2NLSihpYQmzaBhKG46WBhARZAACQ0gC7fuP2igBbq1bGd8srGEhYga9IAAAgJSUEZ944XXw1akruGwvNADevhCmrtPDqQMIABFkAAJBUgO1144yg9V4aNW+1hl4aSBgGQRYAAKQkwHpx1LxT5BcUu7LnliALAADim4XA5zOXQ5XRn4jXR807wXoXXwGMwV4AACC2WQh0AJf+MGreMfJdfgUwgiwAIKP0g1N78/JWbjK3Tv8gdYtu3btLdk5OtQHWj1Hz7rkCmJNRWgAAcPwpTrfW/2VEjRqSHWldSHgNxah5+9vh8iuAEWQBALY4xZnodE1urv+zsgY2Vl4fNW93OS6/AhilBQAAx57idHv9X6ZrYAt27IgrxML+Gru8lpkgCwBw7ClOt9f/ZTLAfjdvXsZ3CenX0OW1zJQWAAAce4rT7fV/GSshKCjI2O4g81q4uJaZIAsAFvLSICX/Kc5wPaiJnuKsLvzWq1vLtHEt8aAU1cDakZf+blKloUtrmQmyAGARrw1S8p/iDPeaEz3FGS0cd9mvhcz5/k/5ZdVmGXxuF6lZs6Y3QpOLA6wX/24QHUEWAFw0gt9rpzgjhWMNsWceva88NGWeFBWXydg3FsqlJ+5p69CUdGB2eYD18t8NIiPIAoAFYhmk5NYP5FSf4vSH441bC+WPdTukbp2a8vPKzZUh1t+ml57S3rahKanA7IEA6+flvxuER5AFAAswSCm1NLz8uX67PPDS3IiPKfg71NotNCUcmD0UYP34u0EogiwAz7OiBtLtk5Rbobo2y65by5ahKe7A7MEA68ffDUIRZAF4mlUDR9Ixgj8ZjhvQlECbSlmhxlnbhaaYA7OHA6xd/25gPS6IAMCzrLwqlJ0mKdcwr/Wk14z+RG5+co651fu63EmitanOWrBtywZbXhGpukDcoU2zyCFWA6xHQqzd/m5gD/TIAvAsqweO2GGScreNAo/UprWkVMrLyy2fLiyeXsbZj54V+ZccFF5T3dtvh78b2AdBFoBn2WHgiNWTlFsd5jPVpgUFpWkNTcmEtdDAnK4Aa0X5SLpKd6z+u4F9EGQBeBYDR+wR5u2uutCUirCmjxs16Mi09cBaUQvutt5+2BM1sgA8K1M1kHZGmLdBnbXWv6axBtaqWvBYevuBZBFkAXgWA0cI85aGtTQH2JTsYxLo7UcmUFoAwNO8PnAkUwOa3CqhsBZlGq38HamvSY5lH5s2iG+O3VjQ249MIMgC8DyvDxzxepjPWFiLEmB73TTT3OZOmZfyulWrAiVzviITKC0AAJjQukfLhtJh76bmlhCbwtKMKCUEGmD9ITZddatWlY9QuoNMoEcWAIA0lGaYWQgGhf+91Wu3mQtPZGLas1jKRxKdnqw69PYj3QiyAACkMKyZK3FF8vcArh0rN2V0IJSVgdLrpTtIL4IsAACpCGs5UU7Rh8xAYEXdKoESbkSNLAAAyUhgGi2mPQNSgyALAEAMdADW6nX5krdyk7lNZh5YBkIBqUFpAQDAcYEy07WegZd4nf3oWZEfGMdFDBgIBSSPIAsAcIzAQBnYi5nquVfDXeLVzEIQSYJX4aJuFfBAaUFpaak88cQT0rNnT8nNzZV+/frJ999/b/VuAfDQaeR0XY8e8QfK0An20zH3aiAdxBUpxOocsDqVFgBrOKJHdvz48fL666/LAw88IHvuuac8++yzMnDgQHn33XelZcuWVu8eAJexotcP1dNT8OGuEpWOuVdjvRJXOqbKgj3KSeAMjgiyH330kZxxxhly1FFHmfu33nqrCbbaK3vSSSdZvXsAPNTrpzWNfIBao7rAmLJAGWOATfclXlGBL5ZwfGlBs2bN5NNPP5XVq1dLWVmZvPbaa1K3bl3Zf//9rd41AC4rI9i4tVD223MXyapbK2KvH6yR9rlXo8xCcNeEL8OGWKbKcmc5CZzDET2yd9xxhwwdOlSOP/54qVWrltSsWVPGjBkje+21l9W7BsCFvT1d9mthel4fmjJPiorLgh7PaWTr+OdeDVdekFSgjNID6x/EdX2EXkGmynJZOQkcxxFBdtmyZdKwYUN56qmnpFWrVqas4Oabb5YpU6ZIx44d496ez+eTgoICSZfCwsKgWwSjfSKjbTLbPjtLRcZM+77KB+XCXyrun9mjrUz7aGnQuvr1aqf1/SMZbj9+tI988LldZOwbC6sESl1eS0qloKA05rbJzsmJ+FwFO3b8/T8V/9Y5dUVuvKCr5BeUSkFRqWRn1ZaG2bWlXu30fp54/djZXk2P6/aCkoy0v13bxw7S1Taa1WpE+5L5txo+faSN/fXXX3LiiSfKpEmTpHv37pXLL7roImnSpImMGzcuru0tWrRIios5FQFApEmLveWGJ76MuP6uKw6TUc99U3k/t30LufSkPWXLpnUZ2kOE0jNyjZo0F6lVXwqKyyRbS0DKCmXblg1SXl4e0za6BXyWhPpu3rwU7i3S/Tf6+NAjZcv6lRndJ2SOlpF26tTJ2T2yCxculJKSkiovpEuXLvLFF18ktM06depIu3btJF30W8mKFSukTZs2Ur8+heihaJ/IaJvMts/v66L3IBSXlFfp9WuQVUN2bdVM7Mi7x092tf8m2jbNmjevtgc2/nN87mDXY0fPmkQrJ2naJFt2bd7Rs+1jB+lqGz0bHwvbB9nWrVub27y8POncuXPl8qVLl5pGS4R2VWdnZ0u66T9oJp7HqWifyGibzLRPg+zg+tdQu7fMkYeHHO246X44fkLoe36kdX+flExVazl9mii7HTu6Jzo7QaT65F0a1fd0+9hJqtsmlrICRwRZDa/dunWTW265RUaOHGmC7cyZM+Wrr76SV155xerdA+DiwUPNGtd3VAhB/IO4UolpotKDS/nC0dNvaT2UXhDh8MMPl9tuu03OPvts+frrr03NrJYXAECi9INQQ4aGjUCMRne4KNNomRKCNIRYpolKL/1b3KNlQ+mwd1Nzy98mHNMjqxo3bmx6Y/UHAOzQ2+P0U8he64HVALtkyZK01cAyTRRgDUcEWQBIJw0Y8YQMTiE7sIQgzVM0ZeyqYwCcVVoAAHbCKWSb0IAapYTArM/g7JJpv+oYgLAIsgCQ4lPIyECArVnTFgE2dOBgOFzGFkgfgiwAxIFTyBbRix3YMMD6MXAQsAY1sgAQB04hZ1hZmUjtKB9VNro4JdNEWYsBmN5EkAWAFM49yynkFCkt1cswOiLAJjNwEKnBAEzvorQAAOLAKeQ0Ky6uKCGIFGItLiGA/TAA09vokQWAOHEKOQ2KivQal5HXE16DcBr9f5jD19sIsgCQAE4hpyh06ZW2GjSI/MsE2Co4jR6MAZjeRpAFAGQ8dA05pa0037t15F8kwCZ0Gl3PFHjtCxYDML2NGlkAQMZCV07Rdhk16MjIIZYa2KiYx7gq5vD1NnpkgRDUngGpD12NC7bIlKcvi/xgwmtMOI0eeQBm2J5/BmC6HkEWCEDtGZA6Gqqa5m+UF5+9IqMBNtyX0VriDpxGD48BmN5FkAX+Ru0ZkEIrVkiHffaRFyOs7nXTTBl/y3GyR4a+jA4+t4vUjHRVMAdhHuPIGIDpTc7/qwZShNozIAV++aViHth99okYYPUnHaEr2pfRsW8slEZNmovTMY8xEIweWeBv1J4BSfjpJ5GDDoq4WsNrukNXdV9GLz2lvbgBp9GB/yHIAg6qPWMgGmxnwQKRgw+OvN7nM8ft+Awct9V92SwoLhO3cPJpdN7HkEoEWcAhtWcMRIOtfPutyGGHxTSIK1Ohq7ovm9l13TLky7l4H0OqUSMLOKD2jGuJwzbmzKmogY0UYi2cB7a6+USlrDDj+4T/4X0M6UCPLOCA2jOuJQ7LffihyEkn2Xoe2GjzieqsBX+sXCq7tmpm6T56Ge9jsEWQ3blzp9Sr593pPeB+dqw9YyAaLPP22yK9etk6wMbyZbSWlEp5ebnVu+dpvI/BFkH2yCOPlNNPP13OOecc6dy5c1p2CoDzBqJZgUEjafTGGyLnneeYAFvdl9GCglLL9gcVeB+DLYLs5ZdfLm+99ZZMmzZN9tlnHzn77LOld+/e0qJF+LokAO4fiGYFBo2kyeTJIpdc4sgAC3vjfQy2GOx17bXXygcffCBTp06Vbt26yYQJE6Rnz55y1VVXmeUlJZwaALw0EM0KO0uFQSOp9sILFYO4IoVYCwdx2Z0eb6vX5Uveyk3mluMvPN7HYKvBXgcffLD5GTFihMyZM0cmTZokN9xwgzRq1Mj00vbv319233331O4t4GF2HYhmhfyCUgaNpMq4cSLXXRd5PeE1Ks4MxIf3Mdhq+q2//vpLnn/+eXnyySdl7ty50qZNGxNiv/jiCznttNPk3XffTd2eAjBv9nu0bCgd9m5qbr365l9QxKCRpI0eXdEDGynE0gNbLaaTSgzvY7C0R3b79u2mhGDmzJny3XffSVZWlpxyyikycuRI00OrbrnlFhk0aJDcd999JtACQCplZzFoJGEjR4qMGhV5PeE1ZkwnBTh01oLi4mLp0qWLjBo1ygTV7OzsKo/r1KmTLF68OFX7CQCVGmbXZtBIvIYNE3n44cjrCbBxYzopwIFBtl+/fnLuuefKvvvuG/VxAwYMkGuuuSaZfQOAsOrVlogT3zNoJISWDmgdbCQE2IQxnRTgwCA7fPjwmB6Xk5OTyP4AgCcGjaR9DtzLLhN58cXI6wmwSWM6KcB6XKIWgGPCXk5WLalZs6ZtrsKWaBhN50j3fYcPl+xPPon8AAJsRi6Jy5kBIDMIsgBsKVLYG3hGB7GDRMNodSPdtZc5oQB06qmS/f77UnXEwt8IsGnh9DMDgKen3wKAdIgW9ibOzjMXRHDqtEuxjHSPy1FHVUyj9f774dczjVbaMZ0UYB2CLADbiRr2lq43F0SwUjJhNGUj3bt0qQiwX34Zfj0BFoAHEGQB2E51Ya6gyNogm0wYTXqku84YowH2hx/Crv5u3jwp2LEj+jYAwCUIsgBsp7owl51lbXl/MmHUP9I9nKgj3Vu0qAiwy5eHX+/zEWABeA5BFoDtRA177VuYCyJYKeEwGjDSPfT3I450z8qqCLAbNoTfICUEADyMWQsAOGpaI521QC+I4ORpl2Ia6a7hNRrCKwAQZAHYU7iwp/PIrvg1T5o36eD4aZcizoFLgAWAmBFkAdhWaNgrKCiQ8vJysYuUXpCBAAsAcSPIAoCVCLAAkDCCLABYgQALAEkjyAJAJhFgASBlCLIAkAkEWABIOYIsAKQTARYA0oYgCwDpQIAFgLQjyAIulF9QnPD8ppncpisRYAEgYwiygMus31IY9opTeiUqncTfLtt0HQJsRvHFCoAiyAIu+3APDZxK7+tyvRJVvB/26dimqxBgM44vVgD8alb+HwDH0x6q0MDpp8t1vR226ZoAGy3EaoAlxKZcdV+sdD0A7yDIAi6ip1mTWZ+pbToaAdZSfLECEIjSAsBFtFYwmfWZ2qYjUUJgC3yxAhCIHlnARXTAi9YKhqPLdb0dtuko9MBmjJYFrF6XL3krN5nbcGUCfLECEIgeWcBFdNCVDngJNxBmSN/chAZlpWObjhAtvGZni+zYkcm9cb1YB3D5v1iFKy/wxBcrAEEIsoDL6Ie+ziSQyqmJ0rFNRwbYVq1E1qzJ5N54QjwzY3j2ixWAsAiygAvph3mqP9DTsU3HBNi2bUWWLcvk3nhq3tdYBnAFHnue+mIFICqCLCzRpGlL2bCtVArXb+JDCPYNsIcfLvLVV5ncG0+WDSQygMv1X6wAxIQgi4zbXuSTSf9ZJd8vnV+5jMnMYasAe/zxIh99lMm98XTZAAO4ACSKWQuQ8Q+1sW8slO+XMpk5Eh+5nrZZCE47rWIGAkJsysRSNuD5mTEAJIweWWRUvLVw8NY17dN66dFoPbB9+ohMn57c9hFWLGUDe7RsyAAuAAkhyCKjmMzcWTJ1TXsNy9sLimX8mz/IgqWRT0HXSnWA7ddPZMqURLaKGMVaNsAALgCJoLQAGUUtnHNk6pr2GpYfmjJPVq/bXiXEJnXp0WglBFddVVFCQIhNu3jKBjS0au9sh72bmltCLIDqEGSRUdTCOUcmrmkfGJaLS8pT01sfLcAOHVoRYCdMSGBvkQj/vK+hf/eUDQBIBUoLkFH6oTX43C4y9vWFQb1vfKh5swwkMCzXrVMzud76aCUEt90mct99Ce0jkkfZAIB0Icgi4xpk1ZBLT9pTBvY+SAp3lvKh5uEykMAw/PPKzdJlvxay8Jdolx4tjS/AjholMmJE0vuJ5DHvK4B0oLQAltiyaZ00b1SbWji7TU2V4TKQwDA864tf5cyj9zVhNvS5wvbWRysheOihihICQiwAuBo9soBDZGoGAb9MXNPeH5Z1+0XFZWbQ15k92krvHvuKT0RaN82WXRplBT1Xt+7dI29wzBiRwYOT3i8AgDMQZAGXXB0pHb3a6a5tDA3LGmanfbS0Miw3DwzoNWpIdqQNTZokcumlKdknAIBzEGQBB7DyQhLprm2sNixHq4F95RWRCy5I274BAOyNIAs4gNsvJBE2LEcJsDtffVXqnX9++ncMAGBrBFnAATx1IYkoAbZo+nT5aa+9pGPHjhndJQCAPTFrAeAAnriQRLRZCD7+2MxCUH7yyZneKwCAjRFkAQdw9dWRogXYOXMqptE67rhM7xUAwAEoLQAcwnVXR4o2iOubb0QOPTSTewMAcCCCLOAgrrg6UrQAu3ChSOfOmdwbAICDEWQBWB9gFy8WYQAXACBOBFkA1gXYX34Radcuk3sDAHARgiyAzAfYFStE9t47k3sDAHAhgiyAzAXY1atFdt89k3sDG1xe2TUDFAHYDkEWQPoD7Jo1Iq1aZXJvYIOQuX5LoYyZtiDo8so6ZZxOJaezcABAsgiyANIXYNevF2nePJN7gwyFzOpCsK4P3b7S+7pcp5KjZxZAsgiyAFIfYDdvFmnSJJN7gzgkGzJjCcEackO3H/g8up4gCyBZXNkLQOquxLVtW8WVuAixthZLyEw0BOt6pT210VS3HgBiQZAFkHyA3b69IsA2bJjpvUICkgmZsYZgLTeIprr1ABALgiyAxANsYWFFgM3JyfReIQnJhMxYQ7DWzGq5QTi6XNcDgGeC7MyZM+W0006TTp06yemnny7vvfee1bsEeDfAFhVVBNisrEzvFVIgmZAZawjW+letmQ19Hr0/pG8u9bEAvDPY66233pI77rhDbr/9djn66KPlnXfekZtuuklat24tubm5Vu8e4J1BXMXFInU4Jex0/pAZbsBWdSHTH4LDlReEhmAd+KUDx5hHFoBng6zP55MnnnhCLrnkEunXr59Zds0118i8efPk22+/JcgCmQiwpaUitWplcm+QZomGzHhDsN4nuALwbJBdvny5/PHHH9KrV6+g5c8995xl+wR4JsCWlYnUdEwFEuKUaMikpxWAXTgiyKqCggK54oorZPHixbLHHnuYXtnjjjsu4V5e3V66FOoAmIBbBKN97NE22VEGaBXoLAQacLUW1kY4duzTPto/37RBLfNToVQKCkrFrjh2oqN9oqN9Mt82mtVqROto+VsNnz7S5vWxw4cPN+F18ODBsv/++8sHH3wgTz/9tLzwwgtyxBFHxLW9RYsWSbHW+QEe1a1794jrvps7N3oPLQAAGVK3bl0zyN/RPbJ1/h5Yor2xffr0Mf/fsWNH0zObSJD1b7Ndu3aSLvqtZMWKFdKmTRupX5/riYeifaxpm6g9sDt2mNuOYm8cO9HRPpHRNtHRPtHRPplvm2XLlsX0ONsH2VatWpnb9u3bBy3XIPrZZ58ltE3tqs7OzpZ003/QTDyPU9E+GWqbaD2sf5+Qcdq/gtOOHb3aVSbrSZ3WPplE20RH+0RH+2SubWIpK3BEkD3wwAMlJydHFi5cKN0DTokuXbpU9tprL0v3DbC1GAIs0m/9lsKwI/x15L8OmgIAJM72w5GzsrJk4MCB8tRTT8nbb78tv//+u4wfP16+/PJLGTBggNW7BzjrQgYaYAmxGe2JDQ2xSu/rcl0PAEic7Xtk1bXXXmu6rB977DFZu3attG3bVsaMGSOHHXaY1bsG2Ac9sLaj5QThLhygdLmuZ8oqAHB5kFXa+0oPLJxYr5h2BFjb0mMsmfUAAJcEWSAVXFWvSIC1Pf2ilMx6AIDDa2SBVHFNvSI1sI6hvf36RSkcXa7rAQCJI8jCM2KpV7Q1AqzjaMmK9vaHhlm9P6RvruNKWvTL3up1+ZK3cpO5dcyXPwCuRWkBPFNf6th6RUoIHE1LVob17+7YvxtXluUAcA2CrIulOnQ6/YPMcfWKBFjX0L87pwXXeMpyNKg7+fUBcC6CrEulOnS64YPMX68YrrzATvWK3QIu/FEFARYWYBoxAHZFjawLpWNQU7rqS1NRcxfrNmxfr6iXTs7JCb+OGlhYyLFlOQBcjx5ZF0pH70mqP8hq1qwp24t8MvaNeUn1Gsfb82zLekVKCGBzjivLAeAZ9Mi6UDp6T1L9QdaoSXMZ+8bCpHqNE+151tC6R8uG0mHvpubWshAbZRaCgh07CLGwDaYRA2BXBFkXSkfvSco/yGrVT7pUwbHTaVUTYL+bNy/juwREY/uyHACeRWmBC6VjUJP/gyzcafxEPsgKisuS7jV2XN1eLCUEBQUZ2x0gHrYsywHgeQRZF0p16EzHB1l23VpJ9xo7pm6PGli4hNOnEQPgPgRZl0pX70nKPsjKCpPuNbb9dFoEWFdz8sVBAMAtCLIuZufek21bNsjgc7tUGfAVT69xunqek0aAdb1k5mkmAANA6hBkYYny8nJpkFUj6V5jW9XtEWA9IZmLgzj96ngAYDcEWdim19jfU/Xn+u1xBVLLe54JsJ6S6DzNbrg6HgDYDUEWtuDInioCrCclOlsGl3kFgNRjHlm48pK6Vs0D69ZLyabiUsJukehsGY6bLg4AHIAeWVjOMT1VHu2BdWRveRolOluGY6aLAwAHoUcWlrN9T5UHe2Ad21tu46tccZlXAEg9emRhOdv2VHm0B9aRveUZlshsGbadLg4AHIwg6wF2n7fSdhc2IMA6p7fcQonMlmGr6eIAwAUIsi7nhPpG2/RU2STANmnaUjZsK5XC9ZssDzq27S13MMuniwMAFyHIupiT5q20tKfKJgFWbS/yyaT/rJLvl863xRcP2/WWAwAQgMFeHq9vtBMNrXu0bCgd9m5qbtMeYm02iEu/eOgle79fap+BVYkObAIAIBPokXUx6hvt3wPrhIFV1HUCAOyKIOtiydQ32n2AmJsCrBO+eFDXCQCwI4KsiyVa3+iEAWJuCrB+DKwCACA+1Mi6WCL1ja6aAN9mNbDVYcJ8AADiQ4+sy8Vb32jXOk039sCG0nYdfG4XGfv6QlkQMOCLgVUAAIRHkPWAeOob7Vyn6dYAG6hBVg259KQ9ZWDvg6RwZ6l76pMBAEgDgiycX6fpggAbaMumddKxYzPJzm5k9a4AAGBr1MgiZXWaWj+7el2+5K3cZG7TXk/rsBpYAACQWvTIIiWXi83oTAcu64EFAACJIcgi6QFiGbsULgEWAAAEIMgi6QFiaZ/pgAALAADCIMgiaWmb6YAACwAAoiDIwn4zHRBgAQBADJi1APa4IpUGVGYhAAAAcSDIwpJL4VYJsDUjHIoEWAAAEAGlBbBkpgMNp9k5OZE3SHgFAADVIMgiszMdlJebANst0noCLAAAiBFBFplRViZSO8rhRoAFAABxIsgivQiwAAAgTRjshfQoLa0YxBUhxH43b54U7NiR8d0CAADuQY8sUqukRKRu9FkKCgoKRJYsEafSS/LGPKgNAACkDUEWqVFcLFKvnutLCNZvKZQx0xYEXZJXpxnT6cd05gYAAJA5lBZ4kPYorl6XL3krN5lbvZ+wnTsrSggihVgXzQOr7RQaYpXe1+VJtSMAAIgbPbIek7IexaIikfpRHu+S8BpIywlCQ6yfLtf1lBgAAJA59Mh6SEp6FAsLK3pgI4VYF/XAhtKa2GTWAwCA1CLIekgsPYpRe2A1wGZney7A+unArmTWAwCA1CLIekhCPYoe7oENpbMTaBlGOLpc13uiLhoAAJugRtZD4upR3L5dpGHDyA/2SHgNpPWvWkscrsZ4SN9c29bHMtMCAMCtCLIe4u9RDFdeUNmjuG2bSOPGkTfiwQAbSIPfsP7dHTOPbHV10fpa7LrvAABUhyDrIdF6FIeevK80zIlvHlivXhhAX6NTXiczLQAA3Iwg6/EexQY7t8vuHfaOuwfWq6ernRbeq6uL3rpda2Xzbf86AAAIhyDrQaZHsTBfpE3zhEoIvHq62onhvbq66IKiErn1qf/a/nUAABAOsxZ4zcaNFbMQNG+e8CwESU3j5VBOvapXtJkWuuzXQn5eudkRrwMAgHAIsl6xYUPSAdbLFwZwanj310WHhlkNsWceva/M+uJXR7wOAADCobTACz2wkcJry5Yia9fGvUkvXhjAyeE9sC5aa2K1nEB7Yh+aMk+Kissc8zoAAAhFkHWrzZtFmjYNv659e5G8vPRO4+UyTg/v/5tpId/UxDr1dQAAEIjSArfZtKmihCBciD3llIrygSRCbLTT1Xa/MIBXr+rlxtcBAICiR9ZNNbAtwgcUueQSkRdf9PSFAbx6VS+3vg4AABRB1unWrRNp1Sr8uuHDRUaPTttTO+nCAKnglvDultcBAABB1ql0kFbr1uHX3X67yL33ipPZ9cIDbgnvbnkdAABvI8g6zV9/iey2W/h1d90lcvfd4nROvPAAAADIPAZ7OcX69RWDuMKFWA2vOojLBSHWqRceAAAAmUePrJNrYO+7T+S228RrFx5w0ynx0BKKnKxaUrMm3y8BAIgFQdau1qwR2XXX8OueeEJkyBBxIydfeCBVJRQDz+hg6X4BAOAUdP3YsQZWSwjChdhXXqkoIXBpiHXDhQdSUUIxcXae7Cy1bNcAAHAMgqxd/PFH5BrYadMqAuwFF4jbeWXC/qglFEvXS34BSRYAgOoQZK22fXtFgN1jj6rr3nyzIsCed554hVeuGlZdiURBEUEWAIDqUCNrlZISkSlTRC6/vOq6GTNEzjpL3CDcfLC1qvkdL0zYX12JRHYWf5oAAFSHT0urAuy//y3y22/B62bNEunVS9w+mGnwuV2qHZnv9gn7/SUU4coLctu3kIbZ/GkCAFAdSgsyGWCff16kQ4eKXlgNsS1bijz4YEV5gZYQuCjERhvMNPaNhdKoSXPxsmglFAN7dZB65FgAAKrFx2UmAuxLL1VcMnb58oplGmBvuUXk6qtFsrPFjaqbD/bSU9qL14UrodB5ZFf8mifNmzAFFwAA1SHIplNpqcjRR4vMn19xXy9soAF20CDXBtiYBzMVl2VsX+wstISioKBAysvLLd0nAACcgiCbTjobwaZNFQH21ltFrrrK9QE25sFMdasb8gUAABAdQTadatUS+eWXilsNtR4SdTCT1oWWFWqctWTfAACAOzDYK91q144aYnVQ1Op1+ZK3cpO51ftuH8yksxZs27LBsn0DAADuQI+sDaen0gCoA4GcMCdstCmyIs0HW0tKqQMFAABJI8jacHoqXa4B0E7zqCYausPNB1vA5VcBAEAKUFpg0+mpdL1TQrdbyiEAAICz0CObJnrlqp2lIpvW5Yc9FV/d9FTVrbdb6LZT7zEAAPAGRwXZ5cuXy9lnny0jRowwt3bWrNVe8tir30c8FV/d9FTVrc8kJ4VuAADgHY4pLSgpKZGbb77ZTBhvd9oT++zsvKin4v3TU4Wjy3W9XTgpdAMAAO9wTJAdM2aMNGjQQJwgv6BUvl9a/an4SNNTDemba6tT9U4K3QAAwDscUVowd+5cee2112TmzJly7LHHit0VFMV2Kj7S9FR2CrHKH7rDzVpgt9ANAAC8w/ZBdtu2bTJ8+HC58847Zdddd03JNn0+X1pLFOrXi3751fr1alc+vz6yaYNa5qdCqS2np8qpK3LjBV1Nb3NBUalkZ9WWhtm1pV7t+NuysLAw6Bb/Q9tER/tER/tERttER/tER/tkvm00q9WI4aqotg+y//rXvyQ3N1d69eqV0nrbJUuWSLo0adpSctu3kAVhygt0eUnhVlnyxzpxsh06t2yS21ixYkWK9sZ9aJvoaJ/oaJ/IaJvoaJ/oaJ/Mtk3dunWdHWS1lGDevHkye/bslG63Tp060q5dO0kX/VYysFcHmfh2RU1s6OVZG2TVkF1bNROv0vbRA75NmzZSv779rmBmJdomOtonOtonMtomOtonOton822zbNmymB5n6yD75ptvysaNG6vUxY4cOVLeffddmThxYkLb1a7q7OxsSafff88zp+J3FJXZuv7VSnrAp/vfwalom+hon+hon8hom+hon+hon8y1TSxlBbYPsg8//LAUFRUFLTvppJNkyJAhcuaZZ4qdlZeXS73aIru0bGj1rgAAALiSrYNsq1atwi5v1qxZxHVOovPJ2n3GAgAAALuydZB1s/VbCsNOZ+W/8hcAAABcckEEv7y8PNtfnjaWntjQEBt65S8AAAC4LMi6gZYThIbY0Ct/AQAAIDqCrAX8V/ZKdD0AAAAIspbQgV3JrAcAAABB1hI6O4EO7ApHl+t6AAAAREeQtYBOsaWzE4SGWb0/pG8uU3ABAADEgOm3LKJTbA3r3515ZAEAABJEkLWQhlYvBNfQCz/kZNWSmjU5GQAAAJJDkIUlF34YeEYHS/cLAAA4H91isOTCDxNn58nOUst2DQAAuABBFtZc+GHpeskvIMkCAIDEUVrg4FpTuw8Oq+7CDgVFBFkAAJA4gqzDa011Gi+dAcGOqruwQ3YWhx8AAEgcpQUOrzXV5brejurWqSVd9otw4Yf2LaRhNkEWAAAkjiDr9FrTvPVmvd1ouH5mxg9y5tH7Vgmzen9QnwOlHjkWAAAkgSjhANXVmla33goarr/5aa0s/GWDnNmjrfTusa8Ul5RL3To15eeVm2XnzhKRBtFLDwAAAKIhyDpAdbWm1a23gj9cFxWXybSPllZZ33W/pp4b/AYAAFKLIGuxWMKYLtOBXeHKC3S5rnfcQK+6tTw3+A0AAKQWQdZCsYYxDba6LNxjh/TNtWUvZHXhW8oKNc6mZfDbsP7dbdkmAAAgtQiyFok3jGmw1WVOOZUeLXwPPreL/LFyqezaqlnaBr/ZtV0AAEDqEGQtkkgY0/tOCmiRwnctKZXy8nJPDX4DAACpR5C1iFfCWLjwXZDkpWmdOPgNAACkHvPIWoQwlnz9bTh2HfwGAABSjyBrEcJY8vW3oe1n58FvAAAg9SgtsIgTZyKwE6cNfgMAAKlHkLUQYSw5Thv8BgAAUosgazHCGAAAQGKokQUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSFyiFjHJLyiWrdt3yo7CEsmpX0caN6jHpXUBAIClCLKo1vothTJm2gJZkLe+clluhxZyfd9cadGkvqX7BgAAvIvSAlTbExsaYpXe1+W6HgAAwAoEWUSl5QShIdZPl+t6AAAAKxBkEZXWxCazHgAAIF0IsohKB3Ylsx4AACBdCLKISmcn0IFd4ehyXQ8AAGAFgiyi0im2dHaC0DCr94f0zWUKLgAAYBmm30K1dIqtYf27M48sAACwFYIsYqKhleAKAADshNICAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5Uw+fz+cRD5s+fL/qS69atm7bn0O2XlJRInTp1pEaNGml7HqeifSKjbaKjfaKjfSKjbaKjfaKjfTLfNsXFxWZ7Bx98cNTH1RaPycQBqM+RzqDsdLRPZLRNdLRPdLRPZLRNdLRPdLRP5ttGtxtLZvNcjywAAADcgRpZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSARZAAAAOBJBFgAAAI5EkAUAAIAjEWQBAADgSATZGJSXl8uTTz4pRx99tHTt2lWuvPJKWbVqVcTHb968Wf75z3/KIYccIoceeqjcfffdUlhYGPSY9957T0477TTp3LmznHXWWfLVV1+JV9rnl19+kauuukoOO+wwOeKII2TIkCHy559/Vq4vKysz7dKhQ4egnzFjxojb22bWrFlVXrf+rF69Wrx+7Oi/f7i20Z/bbrut8nEDBgyosv7iiy8Wp5swYUK1r8Nr7z3xtI2X3ncSaR+vvffE2jZefN/ZsmWL3HXXXdKjRw85+OCD5cILL5R58+ZFfLweI4MGDTKPPeqoo+Txxx83f0+Bpk6dKscff7w5di666CJZvHhx6nbYh2qNGTPGd9hhh/k+/fRT35IlS3yXX36576STTvLt3Lkz7OP79+/vO+ecc3w//vij7//+7/98PXv29A0fPrxy/VdffeU78MADfS+++KJv2bJlvgceeMB30EEHmf93e/ts2rTJd+SRR/quv/56X15enm/RokW+fv36+U499VRfUVGReYy2Q/v27c221q1bV/mzfft2n9uPnQcffNAcP4GvW39KS0t9Xj929N8/tF1Gjx7t69q1q+/nn3+ufNwRRxzhe/nll4Met3nzZp+TTZkyxbf//vubYyMar733xNo2XnvfSeTY8dp7T6xt48X3nQEDBvjOOOMM39y5c32//fab7+677/Z17tzZ9+uvv1Z5bHFxsXnPvuqqq8zf1ocffug79NBDfU888UTlY6ZPn25+/6233vL98ssvvmHDhpnHbNy4MSX7S5Cthn6g5ubm+qZOnVq5bOvWreYfZfbs2VUeP3/+fPNmGPjHPWfOHF+HDh18a9asMff1w3ro0KFBv3f++ef7RowY4XN7+0ybNs08vrCwsHLZn3/+adpMP3jVO++84zv44IN9Thdv26iBAwf67rnnnojb9PKxE+qnn34yH6z6Jum3YcMGcyzpOjfQ94xBgwaZD81TTjkl6geu19574mkbL73vJNI+XnvvibdtvPS+s2LFCvNa5s2bV7msvLzcd8IJJ/gef/zxKo/X92r9QrNly5bKZa+++qr5W/J3SGjQ1S9KfiUlJb5jjjnG9/TTT6dknyktqMbPP/8sO3bsMKei/Bo1aiQHHHCAzJ07t8rjtfu9RYsW0rZt28pleoqvRo0a8t1335lTqfPnzw/antLTXeG257b20ceNGzdOsrKyKpfVrFlxGG7bts3c5uXlBbWfU8XbNtW9dq8fO6FGjRol3bt3lz59+gS1n/6t7bPPPuIGP/30k9SpU8ec9u3SpUvUx3rtvSeetvHS+04i7eO1955428ZL7zu77LKLPPPMM9KpU6fKZfra9Mf/txL6vnPggQdK48aNK5cdfvjhsn37dlmyZIls3LhRVqxYEXTs1K5d27Rhqo6d2inZioutWbPG3O66665By1u2bFm5LtDatWurPLZu3brSpEkT+euvv8yBUFBQIK1bt45pe25rnz322MP8BNI/Gv2A0bo+tXTpUiktLZUrrrjChJ1WrVrJpZdeKr179xY3t83WrVvN8aNvDC+//LKpd9R6omHDhpk3SK8fO4E+/fRTWbBggcycOTNouR47DRs2NB82X375pWRnZ8spp5wi1157rfk7dJrjjjvO/MTCa+898bSNl953Emkfr733xNM2XnvfadSokRxzzDFByz744ANZuXKl3H777VUer//+4Y4Lpe87Glojvc/r31kqEGSr4R8oEXow1qtXz/zxh3t8uANXH79z504pKiqKuD1d7/b2CTV58mSZMmWK3HnnndK0adPKQRnaA6CDMfQP5PPPPzdF9SUlJXLuueeKW9tGX7fSkp/777/fHCvjx483hfGzZ882H7KRtue1Y+eFF16Qnj17SseOHat8oGhb6IewDr7QHoEHH3zQDOrRWzfz2ntPMtz8vpMIr733JMqL7zvz5883fwcnnXSSHHvssVXW67Gi4Tf0uFDaJtHe51N17BBkq+E/FVVcXBx0Wkr/AerXrx/28frYUPp4/Zbm/wcOfUyk7bmtffz0DfOJJ54wb5bXXHNN0OjOt99+24x4zMnJMff3339/84bw3HPPOeoDJd620VMtOgpYT+3oaRw1duxY8+Yxffp0Oe+88yq35+VjR4+Fb775xvSohdIekVtuuaXyNFf79u3NKcQbb7xRhg8fLs2bNxe38tp7TyK88L6TCK+99yTCi+87H330kdx8881mNoKHH3445vcdf0DV953A9/l0HTvUyFbD3x2+bt26oOV6X089hdJv8qGP1X9Anc5Cu9L1NJ/+48a6Pbe1j9IeDj1l9fTTT5tvejfccEPQej3w/R8mfvrG4LRTWIm0jfYO+T9IlP6h6ylRPe3HsfO/N1dtpyOPPLLKOj2NFVirpfbbbz9z67TjJ15ee++Jl1fedxLlpfeeRHjtfWfKlCly/fXXmx5o/ZvxfxGO5X3Hf1+PjUTf5+NBkK2Gfitv0KCB+Sbmp/VCOgeav7YqkC7TA1frSfy+/fZbc9utWzfzRqHfbvzL/HT7+q3Y7e2j9Bvq+++/L4888ohcdtllQev0d3WAivYCBFq0aFHlG4Nb2+a1114zgye0Fs1PC+a1UL5du3YcO3/TOj49Rvy1V4G0hy1wbkf/saO9I23atBE389p7T7y88r6TCK+99yTCS+87L7/8stxzzz3Sr18/efTRR6PW+er7jr5n6/Hi9/XXX5svhfoe36xZM1NnHfg+r6Uq2p7R3ufjkpK5D1zu0UcfNXOeffTRR0FzXer8aTrHns4X55/WRaepuOCCC3x9+vTxLVy40My9p3M53nrrrUFT4nTs2NH3/PPPm6lydE46nXLIqfPxxdM+b775ppnaY+LEiVXm5vM/Rud6POqoo3yfffaZb/ny5b4JEyaY9vriiy98bm4bnQ6oe/fuvuuuu863dOlS3w8//OC77LLLzLQn/rkuvXzs+B1//PG+cePGhd3e5MmTTfvofI6///67mVJJ56nV53G6W265JWiaIN57Ym8br73vxNs+XnzvibVtvPa+89tvv5npxfRYCP1b2bZtm5lSS//fP7WWHh96nFxxxRXmPdw/j6zOEe732muvmWNFpyzzzyOr7cM8shmkB7bOgXb44YebeeeuvPJK36pVq8w6vdU3SH2jDJxTTt8U9bH6jzVy5MjKNwO/GTNm+E488URfp06dzAePfy5Dt7ePTrSs98P9+B+Tn5/vu++++8w8czo/Xe/evc0fhxeOHZ3IXtuoW7duZh4+PY70QyaQV48dP31D1A+MaJOc60T3euxokBs/fryvrKzM53ShH7i898TeNl5730nk2PHae088beOl953x48dH/FvRtvr666/N/+tt4NyzeuzocaFfBnW+2dDXrl8ie/ToYdrxoosu8i1evDhl+1xD/5Oavl0AAAAgc6iRBQAAgCMRZAEAAOBIBFkAAAA4EkEWAAAAjkSQBQAAgCMRZAEAAOBIBFkAAAA4EkEWABxEr1GulxPt1auXFBcXV1k/efJkc2nIzz//3JL9A4BMIsgCgIO0bNnSXAd96dKl8thjj1W5vvvo0aNlwIABcswxx1i2jwCQKVzZCwAc6LbbbpMZM2bIpEmT5PDDD5dt27ZJnz59ZJdddpFXXnlF6tSpY/UuAkDaEWQBwIF27NghvXv3ltLSUpk9e7bceeed8uWXX5pwu+eee1q9ewCQEQRZAHCoBQsWSL9+/aRDhw6yePFiefzxx+XUU0+1ercAIGOokQUAh8rNzZVLLrnEhNgTTjiBEAvAcwiyAOBQhYWFZnaCGjVqyFdffSWrVq2yepcAIKMIsgDgUKNGjTLhdezYsVJeXi7Dhg2TsrIyq3cLADKGIAsADvT222/L9OnTZejQoaasYPjw4aZmdty4cVbvGgBkDIO9AMBhfv/9dznrrLOkU6dO8sILL0jNmhV9EldddZX897//lalTp5r6WQBwO4IsADiIXs3rwgsvlNWrV8usWbOkVatWlevWr19vrvjVoEEDmTlzprkFADejtAAAHOSRRx6RH3/80dTHBoZY1aJFC3PVL62b1VsAcDt6ZAEAAOBI9MgCAADAkQiyAAAAcCSCLAAAAByJIAsAAABHIsgCAADAkQiyAAAAcCSCLAAAAByJIAsAAABHIsgCAADAkQiyAAAAcCSCLAAAAByJIAsAAABxov8HjJwx3nHOxi4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "transient": {}
    }
   ],
   "source": [
    "# Plot the data and the regression line using seaborn\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(data=df, x=\"X\", y=\"y\", label=\"Data Points\")\n",
    "plt.plot(df[[\"X\"]], model.predict(df[[\"X\"]]), color=\"red\", label=\"Regression Line\")\n",
    "plt.title(\"Linear Regression Example\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
